摘要
Low earth orbit(LEO) satellite network provides global coverage and supports a wide range of services. However, due to the rapid changes and energy-limitation of satellites, how to meet the demand of the quality of service(QoS) from ground traffic and prolong the lifetime of LEO satellite network is the research emphasis of the investigator. Hence, a routing algorithm which takes into account the multi-QoS requirements and satellite energy consumption(QER) of LEO satellite network is proposed. Firstly, the satellite intimacy degree(SID) and the path health degree(PHD) are introduced to obtain the path evaluation function according to the energy consumption and queue state of the satellite. Then, the distributed routing QER is established through the path evaluation function and the idea of genetic algorithm(GA), which enables each satellite to adjust traffic and realizes the network load balancing. Simulation results show that QER performs well in terms of end-to-end delay, delay jitter, and system throughput.
Low earth orbit(LEO) satellite network provides global coverage and supports a wide range of services. However, due to the rapid changes and energy-limitation of satellites, how to meet the demand of the quality of service(QoS) from ground traffic and prolong the lifetime of LEO satellite network is the research emphasis of the investigator. Hence, a routing algorithm which takes into account the multi-QoS requirements and satellite energy consumption(QER) of LEO satellite network is proposed. Firstly, the satellite intimacy degree(SID) and the path health degree(PHD) are introduced to obtain the path evaluation function according to the energy consumption and queue state of the satellite. Then, the distributed routing QER is established through the path evaluation function and the idea of genetic algorithm(GA), which enables each satellite to adjust traffic and realizes the network load balancing. Simulation results show that QER performs well in terms of end-to-end delay, delay jitter, and system throughput.
基金
supported by the National Key R&D Program of China (2018YFB1201500)
the National Natural Science Foundation of China (61771072)
the Beijing Natural Science Foundation (L171011)
the Science and Technology Research & Development Plan of China Railway Corporation (J2018G004)
